Communications of the ACM - Special issue on parallelism
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Lessons from the implementation of an adaptive parts acquisition ePortal
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Model-guided information discovery for intelligence analysis
Proceedings of the 14th ACM international conference on Information and knowledge management
Spatial Event Prediction by Combining Value Function Approximation and Case-Based Reasoning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Proceedings of the twelfth international workshop on Web information and data management
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One of the major hurdles in maintaining long-lived electronic systems is that electronic parts become obsolete, no longer available from the original suppliers. When this occurs, an engineer is tasked with resolving the problem by finding a replacement that is "as similar as possible" to the original part. The current approach involves a laborious manual search through several electronic portals and data books. The search is difficult because potential replacements may differ from the original and from each other by one or more parameters. Worse still, the cumbersome nature of this process may cause the engineers to miss appropriate solutions amid the many thousands of parts listed in industry catalogs.In this paper, we address this problem by introducing the notion of a parametric "distance" between electronic components. We use this distance to search a large parts data set and recommend likely replacements. Recommendations are based on an adaptive nearest-neighbor search through the parametric data set. For each user, we learn how to scale the axes of the feature space in which the nearest neighbors are sought. This allows the system to learn each user's judgment of the phrase "as similar as possible."